ISSR Methodology Workshop | Introduction to Text Analysis in Python

Instructor: Venkat Dasari

Text analysis is a powerful approach to understanding large collections of documents, from political speeches and news articles to social media posts and historical records. This workshop introduces participants to computational text analysis using R and the quanteda package. Through hands-on examples and real data, participants will learn how to transform raw text into structured data ready for quantitative analysis.

We'll begin with practical text preprocessing techniques: tokenization, lowercasing, removing punctuation and stopwords, lemmatization, and stemming. Participants will then learn to construct document-feature matrices (DFMs)—a key structure in computational text analysis—and explore methods for trimming sparse data. The workshop culminates with applications such as computing term frequencies, identifying top features across documents, and interpreting results for substantive insights.

Learning Objectives:

By the end of this workshop, participants will be able to:

• Load and manipulate text data in Python using pandas

• Organize document collections and associated metadata

• Tokenize text and apply common preprocessing techniques

• Use spaCy for linguistic preprocessing and lemmatization

• Build a document-term matrix using scikit-learn

• Filter a document-term matrix to manage sparsity and focus on meaningful features

• Extract and interpret term frequencies and document-level statistics

• Create reproducible text-preprocessing and exploration workflows in a Jupyter Notebook

Prerequisites:

Prior experience with Python is expected. Familiarity with basic Python commands, functions, and working with libraries will help you get the most out of the session. Some experience with pandas or Jupyter Notebook is helpful but not required. No prior experience with text analysis or natural language processing is necessary.